Identifying and mitigating risks for agricultural injury associated with obesity
Bibliographic record
Abstract
In some occupational contexts overweight and obesity have been identified as risk factors for injury. The purpose of this study was to examine this hypothesis within farm work environments and then to identify specific opportunities for environmental modification as a preventive strategy. Data on farm-related injuries, height and weight used to calculate body mass index (BMI), and demographic characteristics were from the Phase 2 baseline survey of the Saskatchewan Farm Injury Cohort; a large cross-sectional mail-based survey conducted in Saskatchewan, Canada from January through May 2013. Multivariable logistic regression was used to examine associations between BMI and injury. Injury narratives were explored qualitatively. Findings were inconsistent and differed according to gender. Among women (n = 927), having overweight (adjusted OR: 2.94; 95% CI: 1.29 to 6.70) but not obesity (1.10; 95% CI: 0.35 to 3.43) was associated with an increased odds of incurring a farm-related injury. No strong or statistically significant effects were observed for men (n = 1406) with overweight or obesity. While injury-related challenges associated with obesity have been addressed in other occupational settings via modification of the worksite, such strategies are challenging to implement in farm settings because of the diversity of work tasks and associated hazards. We conclude that the acute effects of overweight in terms of injury do require consideration in agricultural populations, but these should also be viewed with a differentiation based on gender.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".